Why the study?
Does the Trees of Predictors (ToPs) machine learning method improve survival predictions in pre- and post-cardiac transplantation patients compared to existing methods?
Does the Trees of Predictors (ToPs) machine learning method improve survival predictions in pre- and post-cardiac transplantation patients compared to existing methods?
The Trees of Predictors (ToPs) machine learning method provides more accurate, personalized survival predictions for cardiac transplantation patients compared to existing risk-scoring methods.
May aid transplant risk stratification; leaves open prospective validation before clinical use.
We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.
No takes yet. Share an insight, caveat, or question.
Yoon et al. (2018) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: